Reinforcement learning focuses on rewarding desired AI actions and punishing undesired ones. Common RL algorithms include State-action-reward-state-action, Q-learning, and Deep-Q networks. RL ...

Deep reinforcement learning has helped solve very complicated challenges and will continue to be an important interest for the AI community.

The Data Science Doctor explains how to use the reinforcement learning branch of machine learning with the Q-learning approach, providing code on how to solve a maze problem for an easy-to-understand ...

Deep reinforcement learning (DRL) is an exciting area of AI research, with potential applicability to a variety of problem areas. Some see DRL as a path to artificial general intelligence, or AGI ...

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. Think of it like training a dog: every time the dog sits on ...

Reinforcement Learning Breakthrough An approach to artificial intelligence that gets computers to learn like people, without explicit instruction. Why it matters Progress in self-­driving cars ...

By combining reinforcement learning (selecting actions that maximize reward — in this case the game score) with deep learning (multilayered feature extraction from high-dimensional data — in ...